Inverse kinematics solution for trajectory tracking using artificial neural networks for SCORBOT ER-4u

Kumar, Rahul; Chand, Praneel

doi:10.1109/icara.2015.7081175

Cited by 15 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conventional robot Scobot [18]; B. New generation robot Baxter [19]. The coordinate system is defined for any robot so that the XY plane is horizontal and its origin is located at the center of the robot base, as in Figures 1A and 1B.…”

Section: 1mentioning

confidence: 99%

Training Robot Manipulation Skills through Practice with Digital Twin of Baxter

et al. 2019

View full text Add to dashboard Cite

<span style="font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;">This study explores an opportunity to engage first-year engineering students in practice with a modern industrial robot Baxter and provides training in spatial skills. We developed a laboratory exercise in which the students operate the robot to perform spatial manipulations of objects. We implemented the exercise on a digital twin of Baxter in the Gazebo virtual environment. The digital twin was calibrated to mimic the physical properties of the Baxter and correctly simulate its spatial manipulations with oriented cubes. The exercise was delivered to a class of 25 students as part of the robotics workshop in the Introduction to Industrial Engineering course. We administered a post-workshop questionnaire with focus on the analysis of the learning outcomes and students' spatial difficulties. The students noted that the workshop and particularly the exercise effectively exposed them to industrial robotics and raised their spatial awareness in robot operation.</span>

show abstract

Section: 1mentioning

confidence: 99%

Training Robot Manipulation Skills through Practice with Digital Twin of Baxter

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Currently, inverse kinematics methods for robotic arms mainly include geometric, analytical, iterative, and intelligent algorithms. The geometric method [2,3] depends on the geometric characteristics of the robot, is less generalized, and is not applicable to robotic arms with complex structures or many joints. The analytical method [4] has high solution accuracy but is computationally complex and has the problem of multiple solutions.…”

Section: Introductionmentioning

confidence: 99%

Inverse Kinematics of Large Hydraulic Manipulator Arm Based on ASWO Optimized BP Neural Network

Lin,

Xu,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

In order to solve the problem of insufficient end positioning accuracy due to factors such as gravity and material strength during the inverse solution process of a large hydraulic robotic arm, this paper proposes an inverse solution algorithm based on an adaptive spider wasp optimization (ASWO) optimized back propagation (BP) neural network. Firstly, the adaptability of the SWO algorithm is enhanced by analyzing the phase change in population fitness and dynamically adjusting the trade-off rate, crossover rate, and population size in real time. Then, the ASWO algorithm is used to optimize the initial weights and biases of the BP neural network, effectively addressing the problem of the BP neural network falling into local optima. Finally, a neural network mapping relationship between the actual position of the robotic arm’s end-effector and the corresponding joint values is established to reduce the influence of forward kinematic errors on the accuracy of the inverse solution. Experimental results show that the average positioning error of the robotic arm in the XYZ direction is reduced from (91.3, 87.38, 117.31) mm to (18.16, 24.67, 27.21) mm, significantly improving positioning accuracy by 80.11%, 71.78%, and 76.81%, meeting project requirements.

show abstract

“…Once the interpolated matrices are obtained, we proceed to change Cartesian coordinates to articular coordinates using the Inverse Kinematic Model (IKM) [22,23].…”

Section: Linear Motion: Movesmentioning

confidence: 99%

Generation and Control of Basic Geometric Trajectories for a Robot Manipulator Using CompactRIO®

Trujillo

Pérez-Ruiz

Serrezuela

2017

Journal of Robotics

View full text Add to dashboard Cite

The utility of a robot manipulator focuses on the ability to locate its end effector in a position with a determined orientation following a specified trajectory. For this, algorithms were used in order to generate and control the movements joints of robot in a synchronized way. The high-level languages to program robots are based on three types of movement: joint interpolation (MOVEJ), linear interpolation (MOVES), and circular arcs (MOVEC), which are used to develop any type of task. In this work, these three movements are implemented in the industrial controller CompactRIO, as part of the reconditioning process of a robot manipulator of five degrees of freedom (5 DOF) whose controller was obsolete. As a result, it will have an interface in LabVIEW where you can view and modify the basic parameters implemented in the industrial controller. In addition, the results of the validation tests of the joint positions and the end effector of the manipulator will be found.

show abstract

Inverse kinematics solution for trajectory tracking using artificial neural networks for SCORBOT ER-4u

Cited by 15 publications

References 9 publications

Training Robot Manipulation Skills through Practice with Digital Twin of Baxter

Training Robot Manipulation Skills through Practice with Digital Twin of Baxter

Inverse Kinematics of Large Hydraulic Manipulator Arm Based on ASWO Optimized BP Neural Network

Generation and Control of Basic Geometric Trajectories for a Robot Manipulator Using CompactRIO®

Contact Info

Product

Resources

About